Cadres d'applications LLM
Un glossaire des principaux cadres de programmation d'applications de grands modèles de langage (LLM).
Read moreby Stephen M. Walker II, Co-Founder / CEO
Mixture of Experts (MoE) is a machine learning technique that embeds smaller “expert” networks inside a larger network. These experts are dynamically chosen for each input based on the specific task. MoE allows models to scale up accuracy and parameter counts while keeping FLOPS constant. It is a form of ensemble learning where the outputs of multiple models are combined, often leading to improved performance.
Despite being around since the 1990s, MoE has recently become more popular as model scaling through wider and deeper networks hits limitations. Companies like Google and Microsoft are rapidly solving MoE's challenges around complexity, training, and memory footprint. Challenges include:
Mixture of Experts works by training multiple models on different portions of the input space. Each model becomes an "expert" on its specific portion. The outputs of these models are then combined, often using a gating network, to produce the final output.
Mixture of Experts can be used in a wide range of machine learning tasks. These include regression, classification, and more complex tasks like image recognition and natural language processing.
Mixture of Experts is significantly impacting AI by enabling the development of more robust and accurate models. By combining the outputs of multiple models, Mixture of Experts often achieves better performance than any single model could. However, as with any machine learning technique, it is important to use Mixture of Experts responsibly to avoid issues around bias and transparency.
Google's 1.2 trillion parameter MoE model GLaM matched GPT-3 accuracy with 1/3 the energy and half the FLOPS. This shows MoE's efficiency benefits at scale. OpenAI's GPT-4 uses 16 experts. The dynamic routing leads to unpredictable batch sizes for each expert during inference. This causes latency and utilization issues. Inference optimization techniques like pruning underutilized experts can help maximize throughput.
GPT-4 utilizes a Mixture of Experts (MoE) architecture with 16 total experts. During each token generation, the routing algorithm selects 2 of the 16 experts to process the input and produce the output.
This provides two key benefits:
Scaling: With 16 separate experts that can be trained in parallel, GPT-4 can scale up to much larger sizes than previous dense transformer architectures.
Specialization: Each expert can specialize on particular types of inputs or tasks, improving overall accuracy. The routing algorithm learns to select the experts that will perform best for each input.
However, the dynamic routing of GPT-4's MoE architecture also introduces challenges:
Variable batch size: Because different experts are chosen per token, the batch size varies unpredictably for each expert. This leads to inconsistent latency and lower utilization.
Communication overhead: Routing inputs to different experts requires additional communication between GPUs/nodes, increasing overhead.
Memory bandwidth: More experts means memory bandwidth is a constraint, limiting the total number of experts.
To optimize GPT-4's MoE:
Pruning can remove underutilized experts to maximize throughput.
Careful expert placement minimizes communication between nodes.
Low-bandwidth algorithms like Top-2 gating reduce memory traffic.
MoE enables GPT-4 to scale up model size and specialization, at the cost of routing challenges that must be addressed by inference optimization techniques.
Un glossaire des principaux cadres de programmation d'applications de grands modèles de langage (LLM).
Read moreThe analysis of algorithms involves understanding the performance of algorithms in terms of time and space complexity. This analysis is crucial in determining the efficiency of an algorithm and can greatly influence the choice of algorithm for a particular task. The time complexity of an algorithm is typically expressed in Big O notation, which provides an upper bound on the time taken by an algorithm as a function of the input size.
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